Introduction
You can feel the tension in every classroom and study group right now. Students are learning faster with AI, and teachers keep asking a blunt question that cuts through the hype, is this acceleration real learning or borrowed competence that evaporates under pressure? To answer it, we need more than hot takes. We need data, and we finally have two strong sources that land on opposite sides of the same coin.
A large multi-study project from researchers at Penn found that practicing with AI can improve writing quality, and the gains stick. Even just viewing a solid AI example helps. On the other side, a neuroscience team at MIT strapped participants into EEG caps and observed something different, when people lean on an AI assistant to produce an essay, their brains offload work, memory suffers in the short term, and ownership drops. We’ll use both to map the real pros and cons of AI in education, then give a playbook for using AI as a coach, not a crutch. The goal is simple, help you get the benefits without paying the long-term cost.
By the end, you’ll have a practical way to talk about the pros and cons of AI in education with students, colleagues, and parents. You’ll also know how to structure practice so that AI in education strengthens, not softens, the muscles that matter.
Table of Contents
1. The Core Debate: Does Effortless Learning Mean No Learning?
A lot of people trust the “struggle builds skill” rule. They’re not wrong. Effort signals engagement, and engagement often builds memory. Yet struggle is not a virtue by itself. The right example at the right time can compress months of blind trial and error into one “oh, that’s how it works” moment.
Two fresh studies let us move past opinions. Penn tracked how different forms of AI exposure shape measurable writing gains over time. MIT measured brain connectivity and memory when people wrote with and without an AI assistant. Together, they give a sharper, more useful picture of the pros and cons of AI in education.
The Penn results show clear improvements in writing skill after AI exposure and at next-day follow-up. The MIT results show reduced neural coupling and recall when AI generates the text. These are not contradictions. They’re two lenses on the same system, product skill versus process engagement, both matter.
1.1 The Pros: How AI Works As A “Coach” To Enhance Skill Development
The Penn team ran multiple preregistered studies on writing practice. Participants who practiced with an AI or even only saw an AI-generated example later wrote higher-quality texts than those who practiced without AI. The gains persisted a day later. Interestingly, “practice with AI” and “see a high-quality AI example” produced comparable improvements, which suggests that the learning mechanism is not brute effort, it’s the utility of the example and the clarity of the feedback loop. That is a classic “learning by example” effect, the same principle great coaches rely on.
On effort, the pros get sharper. When people practiced with AI, they spent less time, pressed fewer keys, and said it felt easier, yet their later writing still improved. Efficiency and improvement can co-exist if the practice task has high informational value. That supports the “AI as a learning tool” view, and it is a key piece in any fair take on the pros and cons of AI in education.
1.2 The Cons: How AI Can Create A “Cognitive Debt”
The MIT study asked a different question, what happens in your head while you write with an AI? Using EEG, the team found that when participants leaned on an LLM condition to write, neural connectivity narrowed to a few local regions.
When participants wrote without AI, connectivity was broader and stronger, especially in networks associated with creative integration and working memory. Participants in the LLM condition also recalled less from their own essays minutes later and reported a lower sense of ownership over the text. This is cognitive offloading in action, and it captures a real downside in the pros and cons of AI in education.
2. The Impact On Memory: A Tale Of Two Outcomes
Memory is where these studies seem to clash. The MIT team saw immediate memory costs, people who used the LLM struggled to quote their own paragraphs shortly after writing them, and their brains showed less low-frequency coordination that typically supports encoding. That’s a real red flag for anyone thinking about the pros and cons of AI in education, especially for courses that demand recall or step-by-step reasoning without assistance.
The Penn team, meanwhile, tested performance one day later. They still saw improvement in writing quality for those who practiced with AI or even only saw an AI example. The takeaway is subtle and useful, AI can help you internalize procedural knowledge, such as structure, clarity, and revision heuristics, even if you don’t remember every sentence the model produced for you. That nuance belongs in every honest review of the pros and cons of AI in education.
3. The Impact On Critical Thinking And Ownership
Teachers worry that easy answers flatten original thinking. The MIT results provide two concrete signals. First, participants in the LLM condition reported lower ownership of their work. Second, their neural activity shifted away from distributed generative networks and toward narrower supervisory patterns, consistent with overseeing an external writer rather than generating content themselves. That is a credible mechanism for what critics call “cognitive debt AI,” and it explains why repeated passive use can dull the impulse to interrogate ideas.
Penn’s data shows the counterweight. Exposure to strong examples can teach abstract principles, for instance how to choose a stronger lead, compress fluff, and order evidence. Those are the ingredients of better critical writing. The net message for anyone weighing the pros and cons of AI in education is not “ban it,” it is “sequence it so that students generate first, then compare against a standard.”
4. A Practical Framework: Use AI As A Coach, Not A Crutch
You don’t need to choose between speed and substance. You need to choreograph the order of operations. Here is a simple framework that aligns with both studies and works across domains, from labs to literature.
Step 1. Practice First, AI Second. Start every assignment with a short unaided attempt. Even ten minutes works. The MIT group’s crossover design shows why, when participants wrote without AI, connectivity patterns were richer, and when they switched to writing with AI, they had a stronger base to evaluate and edit. Make the brain do the first pass. Then compare and revise with help. This single habit reframes the pros and cons of AI in education into a clear sequence.
Step 2. Use AI For Examples, Not Final Products. The Penn data is unambiguous. Seeing a high-quality example yields gains comparable to practicing with an AI that rewrites your text. Build a library of model answers, critique them, and adapt the patterns to your own work. This is the safest path to AI skill development without eroding authorship.
Step 3. Engage Critically. Treat AI output like a talented intern’s draft. Ask it to explain its choices. Challenge unsupported claims. Rewrite key sections in your own voice. If you’re using AI to study, add teach-back moments where you explain the concept to a peer, or to an empty room. You’ll feel the difference in recall and confidence the next day, and you’ll reduce the long-term impact of AI on skills that schools care about.
Step 4. Instrument The Effort. Keep the task slightly hard. Track time on task and the number of revisions before you ask for help. The Penn studies show that lower keystrokes and lower time don’t automatically mean lower learning, but you still want active engagement. Design prompts that demand comparison, justification, or transformation so “AI and learning” stays active, not passive.
Step 5. Close The Loop Without AI. End study sessions with a no-AI recap. Summarize the key ideas as if you were preparing a 90-second talk. That small ritual consolidates memory and gives you a clean read on what you actually learned.
These steps don’t just manage the pros and cons of AI in education. They give students a repeatable way to improve the impact of AI on skills they can carry into work.
5. For Educators And Parents: A New Model For The Classroom
If you run a course or guide homework at home, you can bake this into the workflow.
- Sequence every task with a generation stage, then a comparison stage. Ask for a first draft or outline produced without tools. Then, allow AI as a learning tool for feedback, examples, and rewrites. Keep a short reflection at the end that asks, what did you change and why.
- Grade the process as well as the product. Collect the unaided draft, the AI-assisted revision, and a brief rationale. That incentive structure flips the pros and cons of AI in education toward honest practice.
Assess retention without support. Spot quizzes, oral defenses, whiteboard sketches. If students relied too heavily on assistance, these reveal the gap. If they used AI to study well, these reveal growth. - Teach the meta-skills. Prompt design is not the point. The point is problem decomposition, evaluation, and explanation. That’s how you protect critical thinking while still using AI in education.
6. The Energy And Environmental Cost: The Hidden “Con”
This part rarely makes the classroom slide deck, yet it belongs in any full treatment of the pros and cons of AI in education. An LLM query consumes roughly ten times the energy of a traditional search. In the MIT paper’s appendix, the authors bluntly call out the material cost, and even provide a rough energy breakdown across sessions. That cost will roll downhill to users and institutions over time. It’s one more reason to keep AI exposure purposeful instead of constant.
7. Evidence At A Glance
Table 1. Impact Of AI On Writing Skill Improvement, Studies 2 And 3 (Penn)
| Condition Comparison | Immediate Post-Practice (Test Phase) | One-Day Follow-up | Key Finding |
|---|---|---|---|
| Study 2, Practice with AI vs. No AI | Improved (d = 0.38) | Improved (d = 0.41) | Practicing with AI improved writing quality compared to no AI practice, and the benefit persisted the next day. |
| Study 3, Practice with AI vs. No AI | Improved (d = 0.32) | Improved (d = 0.29) | Practicing with AI led to better writing quality, with benefits lasting through the follow-up. |
| Study 3, See AI Example vs. No AI | Improved (d = 0.36) | Improved (d = 0.32) | Simply seeing an AI example also improved writing quality, and gains were sustained. |
| Study 3, Practice with AI vs. See AI Example | No significant difference (d = 0.04) | No significant difference (d = 0.02) | Exposure to high-quality examples appears to be the main driver of improvement. |
In the Penn supplement, example-only conditions improved next-day performance, and practice with AI and example-only conditions were statistically indistinguishable on learning gains.
Table 2. Effort Expended During Practice, Studies 2 And 3 (Penn)
| Condition Comparison | Effort Metric | Key Finding |
|---|---|---|
| Study 2, Practice with AI vs. Practice without AI | Less effort, time d = −0.12, keystrokes d = −0.44, subjective rating d = −0.31 | Participants used less time, fewer keystrokes, and reported less effort during practice with AI, yet still learned more. |
| Study 3, Practice with AI vs. Practice without AI | Less effort, time d = −0.14, keystrokes d = −0.55, subjective rating d = −0.14 | The pattern holds across studies. |
| Study 3, See AI Example vs. Practice with AI | Far less effort, time d = −0.99, keystrokes d = −1.86, subjective rating d = −0.18 | Example-only exposure is highly efficient, reinforcing the “learning by example” mechanism. |
Penn’s methods detail lower time and keystrokes in AI conditions and even lower effort in the example-only condition.
8. What This Means For Students Who Want The Edge
If you’re a student deciding how to balance the pros and cons of AI in education, here’s the bottom line.
- Do a cold start. Draft your outline or first paragraph without help. It builds the scaffolding your brain needs.
- Pull examples, not answers. Ask for model paragraphs and compare. Apply the patterns, don’t paste the prose.
- Argue with the model. Ask it to defend its choices. Then keep the parts you can justify in your own words.
- Teach it back. Close by explaining the idea without AI. Use a voice memo or a friend. You’ll feel the retention.
- Keep a paper trail. Save your cold draft, the AI-assisted revision, and a short reflection. That’s how you show learning, not just output.
Follow that and you’ll capture the upside of AI and learning without carrying the cognitive debt AI critics warn about. The pros and cons of AI in education shift in your favor when you control the sequence and keep agency.
9. What This Means For Teams And Workplaces
The classroom rules travel well. Teams that care about writing, analysis, and design can adopt the same choreography. Ask for a brief unaided straw-man before the assisted pass. Establish model libraries for key artifacts, proposals, postmortems, briefs. Encourage peer teach-backs in stand-ups. Measure the impact of AI on skills directly by sampling unaided work over time. That gives you a real read on whether you’re compounding competence or just moving faster on the surface.
This is how you turn the pros and cons of AI in education into a durable workplace advantage.
10. Conclusion: Balance The Coach And The Crutch
We don’t have to pick a side in a false war. When used with intention, AI can be an extraordinary accelerant for skill, especially when it shows you crisp, high-quality examples and gives targeted feedback. When used as a one-click ghostwriter, it encourages shallow supervision, weaker memory, and lower ownership. Both can be true.
Design your practice so that brains fire first, then tools refine. Treat AI as a coach that demonstrates form and returns precise notes. Keep a short unaided recap at the end. That approach keeps the pros and cons of AI in education honest. It also gives educators a clear message for students and parents, we use AI to learn faster and deeper, not to skip the learning. If this resonates, test it in one unit or course. Share the results with your department. Make the pros and cons of AI in education the basis for better pedagogy, not another culture war.
If you’re a student, try the five-step routine on your next assignment. If you’re an instructor, rewrite one assignment with the generate-then-compare structure and tell your class why. If you’re a school leader, publish your stance on the pros and cons of AI in education along with simple rules that make the sequence clear. Craft the future you want. The tools are ready. Let’s use them well.
Key sources informing this article include Penn’s multi-study “Coach, not crutch” project on AI-supported practice and MIT’s EEG-based “Your Brain on ChatGPT” study on cognitive offloading and short-term memory impacts.
1) What are the biggest “cons” of using AI in education? Does it really make you stupid?
The main risk is cognitive debt, where you offload thinking to the model. EEG studies link heavy reliance to weaker short-term recall, reduced neural connectivity, and lower ownership of work. It won’t make you “stupid,” yet the pros and cons of AI in education include real downsides if use stays passive.
2) What are the main “pros” of using AI in education? Can it actually make you smarter?
Yes, when used as a coach. Studies show that practicing with AI, or even reviewing a high-quality AI example, improves writing quality and that the gains persist. AI accelerates feedback, models expert-level structure, and helps students internalize better patterns with less effort.
3) What is the “Cognitive Debt” caused by AI, and how does it affect learning?
Cognitive debt is the cost of outsourcing core steps, like idea generation and drafting, to a model. The result is shallower encoding, weaker immediate recall of what you “wrote,” and less sense of authorship. Over time, that habit can erode critical thinking and originality unless you add unaided practice.
4) How can students and teachers use AI as a “Coach” instead of a “Crutch”?
Sequence the work. First, attempt a short unaided draft or solution. Second, use AI for examples, critique, and alternatives. Third, revise in your own words and justify the changes. End with a no-AI recap or quiz. This keeps agency high while capturing AI’s speed and clarity.
5) What do neuroscientists say about how AI affects the brain?
When people lean on AI to write, EEG readings often show narrower network engagement and lower alpha, beta, and theta activity than unaided drafting, which aligns with cognitive offloading and short-term recall dips. This is why the pros and cons of AI in education must weigh brain engagement, not just output speed.